The development and psychometric properties of the grudge aspect measure
Bibliographic record
Abstract
Abstract Grudges are a common response to an interpersonal transgression that have received limited empirical attention. In the current research, we developed a self‐report measure of holding a grudge—the grudge aspect measure. The items were based on key findings from van Monsjou et al.'s (2021) thematic analysis: the six underlying components of holding a grudge identified in their analysis (need for validation, moral superiority, inability to let go, latency, sever ties, and expectations of the future); the cyclical process of holding a grudge which is characterized by persistent negative affect and intrusive thoughts that interfere with one's quality of life; and the definition of a grudge as sustained feelings of hurt and anger that dissipate over time but are easily reignited. Across three studies, we validated an 18‐item scale capturing three aspects of holding a grudge: disdain , feelings of dislike and intolerance for the transgressor; emotional persistence , sustained negative affect such as anger and hurt; and perceived longevity , perceptions of never being able to let go of the grudge. As expected, these aspects of holding a grudge were linked to less forgiveness and greater general unforgiveness, as well as revenge, avoidance, and rumination. Topics for future research are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".